In this work, an Automated Defect Recognition (ADR) model is presented that exceeds current state of the art accuracy [1]} and based on a CNN RetinaNet model [2]} combined with a Feature Pyramid Network (FPN) [3]} in order to improve its performance at different defect scales. Original GDXray dataset images [4]} were also upscaled to improved detectability of smallest defects, without adding a computational penalty on our model. A thoughtful approach was also followed in order to explore the loss function topology to reach best local minima, showing that Single Stage Detector (SSD) models [5]} can exceed the traditionally more accuracy two stage models such as Faster R-CNN [6]} or Mask R-CNN [7]} if appropriate set of hyper-parameters are used. The proposed model also is relatively light and can be run on a conventional laptop with acceptance inference time.
